Results 11 to 20 of about 14,713 (239)
Spatio-Temporal RBF Neural Networks [PDF]
2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST), 2018 Published in 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST)
Shujaat Khan +4 more
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Parallel implementation of RBF neural networks [PDF]
, 1996 This report presents several parallel implementations, on a MIMD machine, of a learning algorithm called OLS (Orthogonal Least Squares) for RBF (Radial Basis Function) neural networks. The sequential version is first described, and a straightforward parallel version is proposed.
V. Demian +3 more
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Multi-Kernel Fusion for RBF Neural Networks
Neural Processing Letters, 2022 AbstractA simple yet effective architectural design of radial basis function neural networks (RBFNN) makes them amongst the most popular conventional neural networks. The current generation of radial basis function neural network is equipped with multiple kernels which provide significant performance benefits compared to the previous generation using ...
Syed Muhammad Atif +4 more
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Gong-kuang zidonghua, 2014 The paper proposed a fault line selection method of small current grounding system based on wavelet de-noising and improved RBF neural network. Fault information matrix is obtained after normalization processing for maximum of absolute value of de-noised
WANG Xiaowei +3 more
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Application of improved PSO-RBF neural network in the synthetic ammonia decarbonization
Journal of Hebei University of Science and Technology, 2017 The synthetic ammonia decarbonization is a typical complex industrial process, which has the characteristics of time variation, nonlinearity and uncertainty, and the on-line control model is difficult to be established. An improved PSO-RBF neural network
Yongwei LI +3 more
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IEEE Access, 2020 With the rapid economic development, urban areas are seeing more and more vehicles, leading to frequent urban traffic congestion. To solve this problem, the forecasting of traffic parameters is essential, in which, road operating speed (hereinafter ...
Chun Ai, Lijun Jia, Mei Hong, Chao Zhang
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Complexity, 2018 The neural network has the advantages of self-learning, self-adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns.
Tiantian Luan +3 more
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A new type of fault diagnosis method of asynchronous motor
Gong-kuang zidonghua, 2014 In view of problem of difficult parameters determination existed in fault diagnosis method of asynchronous motor based on RBF neural network, the paper proposed a fault diagnosis method of asynchronous motor based on RBF neural network optimized by ...
GOU Xijin, XU Jinxia, KONG Lili
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Finite Element Model Modification of Arch Bridge Based on Radial Basis Function Neural Network [PDF]
E3S Web of Conferences, 2019 Compared with other neural networks, Radial Basis Function (RBF) neural network has the advantages of simple structure and fast convergence. As long as there are enough hidden layer nodes in the hidden layer, it can approximate any non-linear function ...
Chen Tongqing +4 more
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An online self-adaptive RBF network algorithm based on the Levenberg-Marquardt algorithm
Applied Artificial Intelligence, 2022 Aiming at the problem that the Levenberg-Marquardt (LM) algorithm can not train online radial basis function (RBF) neural network and the deficiency in the RBF network structure design methods, this paper proposes an online self-adaptive algorithm for ...
ZhaoZhao Zhang +3 more
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